Identification of the Most Critical Factors in Bankruptcy Prediction and Credit Classification of Companies

Document Type : Research Paper

Authors

1 Professor, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

2 Assistant Professor, Department of Finance and Accounting, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

3 PhD in Financial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

4 Assistant Professor, Department of Industrial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

5 Professor, Department of Financial Management, Faculty of Management, University of Tehran, Tehran, Iran

Abstract

Banks and financial institutions strive to develop and improve their credit risk evaluation methods to reduce financial loss resulting from borrowers’ financial default. Although in previous studies, many variables obtained from financial statements – such as financial ratios – have been used as the input to the bankruptcy prediction process, seldom a machine learning method based on computing intelligence has been applied to select the most critical of them. In this research, the data from companies that are were listed in Tehran’s Stock Exchange and OTC market during 26 years since 1992 to 2017 has been investigated, with 218 companies selected as the study sample. The ant colony optimization algorithm with k-nearest neighbor has been used to feature the selection and classification of the companies. In this study, the problem of the imbalanced dataset has been solved with the under-sampling technique. The results have shown that variables such as EBIT to total sales, equity ratio, current ratio, cash ratio, and debt ratio are the most effective factors in predicting the health status of companies. The accuracy of final research model is estimated that the bankruptcy prediction ranges between 75.5% to 78.7% for the training and testing sample.

Keywords

Main Subjects

References

Abdullah, A., & Achsani, N. A. (2020). Bankruptcy analysis of national airline companies in regional Asia after COVID-19 pandemic. Jurnal Aplikasi Bisnis dan Manajemen (JABM), 6(3), 691-691.‏
Antunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing, 60, 831-843.
Arieshanti, I., Purwananto, Y., Ramadhani, A., Nuha, M. U., & Ulinnuha, N. (2013). Comparative study of bankruptcy prediction models. Telkomnika, 11(3),  -596.
Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417.‏
Bayat, A., Ahmadi, S., & Mohamadi, M. (2019). The Bankruptcy prediction of Tehran stock exchange using firefly algorithm (FA). Financial Engineering and Portfolio Management, 9(37), 234-262.
Berg, D. (2007). Bankruptcy prediction by generalized additive models. Applied Stochastic Models in Business and Industry, 23(2), 129-143.‏
Boratyńska, K., & Grzegorzewska, E. (2018). Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches. Journal of Business Research, 89, 175-181.
 Chen, H. L., Yang, B., Wang, G., Liu, J., Xu, X., Wang, S. J., & Liu, D. Y. (2011). A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems, 24(8), 1348-1359.‏
Chen, Y., Miao, D., & Wang, R. (2010). A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letters, 31(3), 226-233.‏
Chen, Z., Chen, W., & Shi, Y. (2019). Ensemble learning with label proportions for bankruptcy prediction. Expert Systems with Applications, 113155.
Cho, S., Hong, H., & Ha, B. C. (2010). A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction. Expert Systems with Applications, 37(4), 3482-3488.
Chou, C. H., Hsieh, S. C., & Qiu, C. J. (2017). Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction. Applied Soft Computing, 56, 298-316.
Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447-1465.
de Andrés, J., Landajo, M., & Lorca, P. (2012). Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios. Knowledge-Based Systems, 30, 67-77.
Divsalar, M., Firouzabadi, A.K., Sadeghi, M., Behrooz, A.H., & Alavi, A.H. (2011). Towards the prediction of business failure via computational intelligence techniques. Expert Systems, 28(3), 209-226.
Djebali, N., & Zaghdoudi, K. (2020). Threshold effects of liquidity risk and credit risk on bank stability in the MENA region. Journal of Policy Modeling, 42(5), 1049-1063.‏
Du Jardin, P., & Séverin, E. (2012). Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time. European Journal of Operational Research, 221(2), 378-396.
Du Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286-303.
Du Jardin, P. (2016). A two-stage classification technique for bankruptcy prediction. European Journal of Operational Research, 254(1), 236-252.
Du Jardin, P. (2017). Dynamics of firm financial evolution and bankruptcy prediction. Expert Systems with Applications, 75, 25-43.
Fallahpour, S., Norouzian Lakvan, E., & Hendijani Zadeh, M. (2017). Use of combined approach of support vector machine and feature selection for financial distress prediction of listed companies in Tehran Stock Exchange Market. Financial Research Journal, 19(1), 139-156.‏
Fallahpour, S., Raei, R., & Norouzian, E. (2018). Applying combined approach of sequential floating forward selection and support vector machine to predict financial distress of listed companies in Tehran Stock Exchange Market. Financial Research Journal, 20(3), 289-304.‏
Fernandes, G. B., & Artes, R. (2016). Spatial dependence in credit risk and its improvement in credit scoring. European Journal of Operational Research, 249(2), 517-524.
García, V., Marqués, A. I., & Sánchez, J. S. (2019). Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction. Information Fusion, 47, 88-101.
Gepp, A., & Kumar, K. (2008). The role of survival analysis in financial distress prediction. International research journal of finance and economics, 16(16), 13-34.‏
Ghasemi, A. A., Seyghalib, M., & Moradi, M. (2018). Prediction of financial distress, using metaheuristic models. Financial and Credit Activity: Problems of Theory and Practice, 1(24), 238-249.‏
Ghazizadeh, N., Abbaszadeh, M. R., Salehi, M., & Jabbari Nooghabi, M. (2019). Comparison of new technologies statistical models and machine learning models to predicting banks failure [paper presentation]. 17th national conference of Iran accounting, Qom, Iran . 
Gordini, N. (2014). A genetic algorithm approach for SMEs bankruptcy prediction: Empirical evidence from Italy. Expert Systems with Applications, 41(14), 6433-6445.
Grunert, J., Norden, L., & Weber, M. (2005). The role of non-financial factors in internal credit ratings. Journal of Banking & Finance, 29(2), 509-531.
Hayashi, Y. (2016). Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective. Operations Research Perspectives, 3, 32-42.
Heo, J., & Yang, J. Y. (2014). AdaBoost based bankruptcy forecasting of Korean construction companies. Applied Soft Computing, 24, 494-499.
Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117, 287-299.
Huang, X., Liu, X., & Ren, Y. (2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research, 52, 317-324.‏
Iturriaga, F. J. L., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of US commercial banks. Expert Systems with applications, 42(6), 2857-2869.‏
Jabeur, S. B. (2017). Bankruptcy prediction using partial least squares logistic regression. Journal of Retailing and Consumer Services, 36, 197-202.
Jeong, C., Min, J. H., & Kim, M. S. (2012). A tuning method for the architecture of neural network models incorporating GAM and GA as applied to bankruptcy prediction. Expert Systems with Applications, 39(3), 3650-3658.
Kanan, H. R., & Faez, K. (2008). An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system. Applied Mathematics and Computation, 205(2), 716-725.
Kim, H. J., Jo, N. O., & Shin, K. S. (2016). Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction. Expert Systems with Applications, 59, 226-234.‏
Kim, M. J., & Kang, D. K. (2010). Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications, 37(4), 3373-3379.
Kiss, T., & Österholm, P. (2021). Corona, crisis and conditional heteroscedasticity. Applied Economics Letters, 28(9), 755-759.‏
Liang, K., & He, J. (2020). Analyzing credit risk among Chinese P2P-lending businesses by integrating text-related soft information. Electronic Commerce Research and Applications, 40, 100947.
Liu, C., Xie, J., Zhao, Q., Xie, Q., & Liu, C. (2019). Novel evolutionary multi-objective soft subspace clustering algorithm for credit risk assessment. Expert Systems with Applications, 138, 112827.
Liu, M., Zhang, F., Ma, Y., Pota, H. R., & Shen, W. (2016). Evacuation path optimization based on quantum ant colony algorithm. Advanced Engineering Informatics, 30(3), 259-267.
‏‏Mai, F., Tian, S., Lee, C., & Ma, L. (2019). Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research, 274(2), 743-758. ‏
Marcinkevičius, R., & Kanapickienė, R. (2014). Bankruptcy prediction in the sector of construction in Lithuania. Procedia-Social and Behavioral Sciences, 156, 553-557.
Nam, J. H., & Jinn, T. (2000). Bankruptcy prediction: Evidence from Korean listed companies during the IMF crisis. Journal of International Financial Management & Accounting, 11(3), 178-197.‏
Nazemi Ardakani, M., & Zare Mehrjerdi, V. (2017). Prediction of firms bankruptcy based on industry characteristics. Quarterly Journal of Asset Management and Financing, 7(2), 122-139.
Nazemi Ardakani, M., Zare Mehrjerdi, V., & Mohammadi Nodooshan, A. (2018). A firms’ bankruptcy prediction model based on selected industries by using decision trees model. Accounting Research, 6(2), 121-138.
Nedumparambil, E., & Bhandari, A. K. (2020). Credit risk–return puzzle: Evidence from India. Economic Modelling, 92, 195-206.‏
Nyitrai, T., & Virág, M. (2019). The effects of handling outliers on the performance of bankruptcy prediction models. Socio-Economic Planning Sciences, 67, 34-42.
Pirayesh, R., Dadashi Arani, H., Barzegar, M. (2017). Mathematical model design for predicting bankruptcy of companies accepted in the Tehran Stock Exchange. Financial Engineering and Portfolio Management, 8(31), 187-200.
Pompe, P. P., & Bilderbeek, J. (2005). The prediction of bankruptcy of small-and medium-sized industrial firms. Journal of Business venturing, 20(6), 847-868.‏
Qu, Y., Quan, P., Lei, M., & Shi, Y. (2019). Review of bankruptcy prediction using machine learning and deep learning techniques. Procedia Computer Science, 162, 895-899. ‏
Shi, Y., & Li, X. (2019). A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms. Heliyon, 5(12), e02997.‏
Shie, F. S., Chen, M. Y., & Liu, Y. S. (2012). Prediction of corporate financial distress: An application of the America banking industry. Neural Computing and Applications, 21(7), 1687-1696.
Sironi, A., & Resti, A. (2007). Risk management and shareholders' value in banking: from risk measurement models to capital allocation policies (Vol. 417). John Wiley & Sons.‏
 Son, H., Hyun, C., Phan, D., & Hwang, H. J. (2019). Data analytic approach for bankruptcy prediction. Expert Systems with Applications, 138, 112816.
Sousa, M., Gama, J., & Brandão, E. (2016). Dynamic credit score modeling with short-term and long-term memories: The case of Freddie Mac’s database. The Journal of Risk Model Validation, 10(1), 59–80.
Tabakhi, S., Moradi, P., & Akhlaghian, F. (2014). An unsupervised feature selection algorithm based on ant colony optimization. Engineering Applications of Artificial Intelligence, 32, 112-123.‏
Tobback, E., Bellotti, T., Moeyersoms, J., Stankova, M., & Martens, D. (2017). Bankruptcy prediction for SMEs using relational data. Decision Support Systems, 102, 69-81.
Tsai, C. F., & Hsu, Y. F. (2013). A meta‐learning framework for bankruptcy prediction. Journal of Forecasting, 32(2), 167-179.
Tsai, C. F., Hsu, Y. F., & Yen, D. C. (2014). A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing, 24, 977-984.
Tserng, H. P., Chen, P. C., Huang, W. H., Lei, M. C., & Tran, Q. H. (2014). Prediction of default probability for construction firms using the logit model. Journal of Civil Engineering and Management, 20(2), 247-255.
Uthayakumar, J., Vengattaraman, T., & Dhavachelvan, P. (2020). Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: An application of bankruptcy prediction and credit risk analysis. Journal of King Saud University-Computer and Information Sciences, 32(6), 647-657.‏
Veganzones, D., & Séverin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111-124.‏
Virág, M., & Nyitrai, T. (2014). Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model. Acta Oeconomica, 64(4), 419-440.
Volkov, A., Benoit, D. F., & Van den Poel, D. (2017). Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination. Decision Support Systems, 98, 59-68.‏
Wang, G., Ma, J., & Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), 2353-2361.
 (2017). Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction. Engineering Applications of Artificial Intelligence, 63, 54-68.‏
Xiong, T., Wang, S., Mayers, A., & Monga, E. (2013). Personal bankruptcy prediction by mining credit card data. Expert Systems with Applications, 40(2), 665-676.
Yang, Z., You, W., & Ji, G. (2011). Using partial least squares and support vector machines for bankruptcy prediction. Expert Systems with Applications, 38(7), 8336-8342.
Yoon, J. S., & Kwon, Y. S. (2010). A practical approach to bankruptcy prediction for small businesses: Substituting the unavailable financial data for credit card sales information. Expert Systems with Applications, 37(5), 3624-3629.
Zhang, F., Tadikamalla, P. R., & Shang, J. (2016). Corporate credit-risk evaluation system: Integrating explicit and implicit financial performances. International Journal of Production Economics, 177, 77-100.
Zięba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications, 58, 93-101.
Zhou, L., Lai, K. K., & Yen, J. (2012). Empirical models based on features ranking techniques for corporate financial distress prediction. Computers & Mathematics with Applications, 64(8), 2484-2496.
 Zhou, L., Lai, K. K., & Yen, J. (2014). Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation. International Journal of Systems Science, 45(3), 241-253.
Zoričák, M., Gnip, P., Drotár, P., & Gazda, V. (2020). Bankruptcy prediction for small-and medium-sized companies using severely imbalanced datasets. Economic Modelling, 84, 165-176. ‏
 
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)
Volume 14, Issue 4 - Serial Number 4
October 2021
Pages 817-834

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